16LP

Technical details

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library(GeoPressureR)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(kableExtra)
library(plotly)
library(GeoLocTools)
setupGeolocation()
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure/", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/2_light/", params$gdl_id, "_light_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
load(paste0("../data/4_basic_graph/", params$gdl_id, "_basic_graph.Rdata"))

Settings used

All the results produced here are generated with (1) the raw geolocator data, (2) the labeled files of pressure and light and (3) the parameters listed below.

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kable(gpr) %>% scroll_box(width = "100%")
gdl_id crop_start crop_end thr_dur extent_N extent_W extent_S extent_E map_scale map_max_sample map_margin prob_map_s prob_map_thr shift_k kernel_adjust calib_lon calib_lat calib_1_start calib_1_end calib_2_start calib_2_end calib_2_lon calib_2_lat prob_light_w thr_prob_percentile thr_gs RingNo scientific_name common_name mass wing_span Color
16LP 2017-01-15 2017-12-20 6 17 9 -25 38 5 300 30 1.2 0.9 0 1.4 28.76931 -22.7204 2017-01-15 2017-04-15 2017-11-14 2017-12-20 NA NA 0.1 0.95 120 NA Halcyon senegaloides Woodland Kingfisher NA NA #FF9770

Pressure timeserie

The labeling of pressure data is illustrated with this figure. The black dots indicates the pressure datapoint not considered in the matching. Each stationary period is illustrated by a different colored line.

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pressure_na <- pam$pressure %>%
  mutate(obs = ifelse(isoutliar | sta_id == 0, NA, obs))
p <- ggplot() +
  geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
  geom_point(data = subset(pam$pressure, isoutliar), aes(x = date, y = obs), colour = "black") +
  # geom_line(data = pressure_na, aes(x = date, y = obs, color = factor(sta_id)), size = 0.5) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = pressure0, col = factor(sta_id))) +
  theme_bw() +
  scale_colour_manual(values = pam$sta$col) +
  scale_y_continuous(name = "Pressure(hPa)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Pressure calibration

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sp_pressure = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0)

sta_plot <- which(difftime(pam$sta$end,pam$sta$start,unit="days")>3)

par(mfrow=c(2,3))
for (i in seq_len(length(sta_plot))){
  i_s = sta_plot[i]
  pressure_s = pam$pressure %>% 
    filter(sta_id==i_s & !isoutliar)
  
    err <- pressure_s %>% left_join(sp_pressure, by="date") %>% 
      mutate(
        err = obs-pressure-mean(obs-pressure)
      ) %>% .$err
    
    hist(err, freq = F, main = paste0("sta_id=",i_s, " | ",nrow(pressure_s)," dtpts | std=",round(sd(err),2)))
   xfit <- seq(min(err), max(err), length = 40) 
    yfit <- dnorm(xfit, mean = mean(err), sd = sd(err)) 
    lines(xfit, yfit, col = "red")
}

Light

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raw_geolight <- pam$light %>%
  transmute(
    Date = date,
    Light = obs
  )
lightImage(tagdata = raw_geolight, offset = 0)
tsimagePoints(twl$twilight,
  offset = 0, pch = 16, cex = 1.2,
  col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
)
abline(v = gpr$calib_2_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_start, lty = 1, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_2_end, lty = 2, col = "firebrick", lwd = 1.5)
abline(v = gpr$calib_1_end, lty = 2, col = "firebrick", lwd = 1.5)

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hist(z, freq = F)
lines(fit_z, col = "red")

The probability map resulting from light data alone can be seen below.

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li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(light_prob))) {
  i_s <- metadata(light_prob[[i_r]])$sta_id
  info <- pam$sta[pam$sta$sta_id == i_s, ]
  info_str <- paste0(i_s, " | ", info$start, "->", info$end)
  li_s <- append(li_s, info_str)
  l <- l %>% addRasterImage(light_prob[[i_r]], opacity = 0.8, colors = "OrRd", group = info_str)
}
l %>%
  addCircles(lng = gpr$calib_lon, lat = gpr$calib_lat, color = "black", opacity = 1) %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Light vs Pressure

We can compare light and pressure location at long stationary stopover (>5 days). By assuming the best match of the pressure to be the truth, we can plot the histogram of the zenith angle and compare to the fit of kernel density at the calibration site.

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 raw_geolight <- pam$light %>%
    transmute(
      Date = date,
      Light = obs
    )
 dur <- unlist(lapply(pressure_prob, function(x) difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1], units = "days" )))
  long_id <- which(dur>5)

par(mfrow = c(2, 3))
for (i_s in long_id){
  twl_fl <- twl %>%
    filter(!deleted) %>%
    filter(twilight>shortest_path_timeserie[[i_s]]$date[1] & twilight<tail(shortest_path_timeserie[[i_s]]$date,1))
  sun <-  solar(twl_fl$twilight)
  z_i <- refracted(zenith(sun, shortest_path_timeserie[[i_s]]$lon[1], shortest_path_timeserie[[i_s]]$lat[1]))
  hist(z_i, freq = F, main = paste0("sta_id=",i_s, " | ",nrow(twl_fl),"twls"))
  lines(fit_z, col = "red")
  xlab("Zenith angle")
}

Similarly, we can plot the line of sunrise/sunset at the best match of pressure (yellow line) and compare to the raw and labeled light data.

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  lightImage(
    tagdata = raw_geolight,
    offset = gpr$shift_k / 60 / 60
  )
  tsimagePoints(twl$twilight,
                offset = gpr$shift_k / 60 / 60, pch = 16, cex = 1.2,
                col = ifelse(twl$deleted, "grey20", ifelse(twl$rise, "firebrick", "cornflowerblue"))
  )
  for (ts in shortest_path_timeserie){
    if (!is.null(ts)){
      twl_fl <- twl %>%
      filter(twilight>ts$date[1] & twilight<tail(ts$date,1))
      if (nrow(twl_fl)>0){
      tsimageDeploymentLines(twl_fl$twilight,
                             lon = ts$lon[1], ts$lat[1],
                             offset = gpr$shift_k / 60 / 60, lwd = 3,col = adjustcolor("orange", alpha.f = 0.5))
        
      }
    }
  }

GeoPressureViz

To visualize the path on GeoPressureViz, you will need to also load the pressure and light probability map and align them first with the code below.

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sta_marginal <- unlist(lapply(static_prob_marginal, function(x) raster::metadata(x)$sta_id))
sta_pres <- unlist(lapply(pressure_prob, function(x) raster::metadata(x)$sta_id))
sta_light <- unlist(lapply(light_prob, function(x) raster::metadata(x)$sta_id))
pressure_prob <- pressure_prob[sta_pres %in% sta_marginal]
light_prob <- light_prob[sta_light %in% sta_marginal]

The code below will open with the shortest path computed with the graph approach.

Show code
geopressureviz <- list(
  pam_data = pam,
  static_prob = static_prob,
  static_prob_marginal = static_prob_marginal,
  pressure_prob = pressure_prob,
  light_prob = light_prob,
  pressure_timeserie = shortest_path_timeserie
)
save(geopressureviz, file = "~/geopressureviz.RData")

shiny::runApp(system.file("geopressureviz", package = "GeoPressureR"),
  launch.browser = getOption("browser")
)

Stationay period information

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pam$sta %>% mutate(duration = difftime(end,start,units="days")) %>% kable()
start end sta_id col duration
2017-01-15 00:00:00 2017-04-14 18:40:00 1 #1B9E77 89.7777778 days
2017-04-15 03:30:00 2017-04-15 18:05:00 2 #D95F02 0.6076389 days
2017-04-16 01:55:00 2017-04-16 17:20:00 3 #7570B3 0.6423611 days
2017-04-16 22:15:00 2017-04-17 16:45:00 4 #E7298A 0.7708333 days
2017-04-17 20:30:00 2017-04-18 16:45:00 5 #66A61E 0.8437500 days
2017-04-19 02:50:00 2017-04-23 21:55:00 6 #E6AB02 4.7951389 days
2017-04-24 03:05:00 2017-05-10 16:45:00 7 #A6761D 16.5694444 days
2017-05-11 03:50:00 2017-05-11 16:55:00 8 #666666 0.5451389 days
2017-05-12 03:35:00 2017-05-12 19:15:00 9 #1B9E77 0.6527778 days
2017-05-12 22:55:00 2017-05-14 21:50:00 10 #D95F02 1.9548611 days
2017-05-14 23:15:00 2017-05-15 00:55:00 11 #7570B3 0.0694444 days
2017-05-15 01:10:00 2017-05-16 00:15:00 12 #E7298A 0.9618056 days
2017-05-16 02:05:00 2017-05-17 22:35:00 13 #66A61E 1.8541667 days
2017-05-18 00:00:00 2017-05-22 23:35:00 14 #E6AB02 4.9826389 days
2017-05-23 00:25:00 2017-05-23 20:25:00 15 #A6761D 0.8333333 days
2017-05-23 22:45:00 2017-05-24 01:05:00 16 #666666 0.0972222 days
2017-05-24 01:35:00 2017-05-25 01:40:00 17 #1B9E77 1.0034722 days
2017-05-25 02:05:00 2017-06-11 20:15:00 18 #D95F02 17.7569444 days
2017-06-11 22:15:00 2017-06-12 21:30:00 19 #7570B3 0.9687500 days
2017-06-12 22:25:00 2017-06-13 18:10:00 20 #E7298A 0.8229167 days
2017-06-13 20:00:00 2017-06-14 21:50:00 21 #66A61E 1.0763889 days
2017-06-14 23:10:00 2017-06-15 00:30:00 22 #E6AB02 0.0555556 days
2017-06-15 00:45:00 2017-06-15 20:30:00 23 #A6761D 0.8229167 days
2017-06-15 21:20:00 2017-10-26 16:35:00 24 #666666 132.8020833 days
2017-10-27 03:40:00 2017-10-27 16:30:00 25 #1B9E77 0.5347222 days
2017-10-28 03:25:00 2017-10-28 16:45:00 26 #D95F02 0.5555556 days
2017-10-29 03:25:00 2017-10-29 18:55:00 27 #7570B3 0.6458333 days
2017-10-30 02:40:00 2017-10-30 19:00:00 28 #E7298A 0.6805556 days
2017-10-30 21:45:00 2017-10-30 23:10:00 29 #66A61E 0.0590278 days
2017-10-30 23:55:00 2017-11-04 16:55:00 30 #E6AB02 4.7083333 days
2017-11-05 01:40:00 2017-11-05 18:15:00 31 #A6761D 0.6909722 days
2017-11-05 22:15:00 2017-11-06 18:25:00 32 #666666 0.8402778 days
2017-11-06 23:05:00 2017-11-07 22:55:00 33 #1B9E77 0.9930556 days
2017-11-08 00:10:00 2017-11-10 19:45:00 34 #D95F02 2.8159722 days
2017-11-10 22:15:00 2017-11-11 21:35:00 35 #7570B3 0.9722222 days
2017-11-11 22:55:00 2017-11-12 18:00:00 36 #E7298A 0.7951389 days
2017-11-13 00:10:00 2017-11-13 17:10:00 37 #66A61E 0.7083333 days
2017-11-14 01:55:00 2017-12-19 23:55:00 38 #E6AB02 35.9166667 days